Science to Data Science: Orientation for PhDs and Postdocs

You are finishing a PhD or holding a Postdoc and have started wondering if you should leave the academic world? Or perhaps you know that anyway. For me it was always clear that I would not pursue an academic career, even though I accepted a two-year postdoc and enjoyed my time very much.

So, if you want to build that bridge to Data Science and Machine Learning, how do you get started?

I suggest you get a first orientation; that is, survey the landscape to better understand where and how to build your bridge. I have four recommendations:

  1. Test-drive a few online courses to get a sense of the practicalities involved such as programming languages and tools. After all, this is what you will be doing daily. You are familiar with learning, and you have experienced the shift to online learning, so this should come natural. Does this new practice suit you?
  2. Select some relevant books and articles for reading about approaches to the field and for getting a sense of the established and upcoming use cases. Much excitement is around neural networks, computer vision, and natural language understanding. However, a focus on neural networks is just one possible approach. Also, autonomous driving is grabbing a lot of headlines, yet that is just one future of mobility, and mobility is just one field. You will find it helpful to focus on the use cases. This is important already because the user or customer (and not your peers) will decide about impact. Moreover, it will help shift perspective. We all are customers, so you can start from your own user experience with something that has ‘AI’ inside, and then ask your friends about their experiences.
  3. Go out and get a feel for what is going on. Even in academic towns there likely will be some regular meetup or event where practitioners gather. If you really can’t find people, do make the effort to go to a conference or meetup in the next biggest conurbation. You could also combine business and pleasure — like you do for those academic conferences. If none of this works soon enough, consider joining an online community that has active communication channels.
  4. Draw up a list of suitable courses and people that you can draw on for building your bridge to data science. You are likely to include online course providers, onsite training camps, potential mentors in your network, and recruiting specialists. This is not unlike your search for the right people and place to start your PhD. Of course, the profile and skills set you are looking for is different. Then again, as a PhDs you likely will have heard about ‘transferable’ skills, and that is a good starting point. Who can help you most in transferring and transforming your current skills so that you get the Data Science career that suits you?

My recommendations are specific but do ask you to do the search and the work. I think it is valuable to spend some time using search engines, platforms, and other tools to get a personal orientation. Of course, we can also engage in a bit of dialogue here. So let me know what you want to know.

This was the second post on Science to Data Science https://medium.com/@chrisarmbruster

If you would like to experience ‘bridge building’ in person, please work with us to facilitate a workshop in a location near you.